دوره 18، شماره 3 - ( 10-1400 )                   جلد 18 شماره 3 صفحات 146-127 | برگشت به فهرست نسخه ها


XML English Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

moradi M, nejatian S, parvin H, bagherifard K, rezaei V. Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic Optimization. JSDP 2021; 18 (3) :127-146
URL: http://jsdp.rcisp.ac.ir/article-1-1025-fa.html
مرادی محسن، نجاتیان صمد، پروین حمید، باقری فرد کرم الله، رضایی وحیده. الگوریتم فرا‌ابتکاری دسته والد-فرزند مبتنی بر حافظه و خوشه‌بندی جهت بهینه‌‌سازی پویا. پردازش علائم و داده‌ها. 1400; 18 (3) :127-146

URL: http://jsdp.rcisp.ac.ir/article-1-1025-fa.html


دانشگاه آزاد اسلامی، واحد یاسوج
چکیده:   (1573 مشاهده)
تاکنون روش­های مختلفی برای بهینه­سازی ارایه شده است و یکی از معروف­ترین روش­های بهینه­سازی، الگوریتم­های هوش­جمعی هستند. بسیاری از مسائل بهینه‌­سازی اخیر در دنیای واقعی طبیعت پویا دارند؛ بنابراین، الگوریتم بهینه‌­سازی برای حل مسائل در محیط­‌های پویا مورد نیاز است. الگوریتم دستۀ والد-فرزند مبتنی بر حافظه و خوشه­‌بندی (CMPCS)، گونه‌­ای از الگوریتم‌­های هوش­جمعی و برگرفته شده از طبیعت است، که در این مقاله ارایه شده است. این روش به رفتار فردی و گروهی وابسته است، در این الگوریتم برای افزایش کارآیی از یک حافظه با خوشه‌بندی و دافعه استفاده شده است. روش CMPCS پیشنهاد شده بر روی محک قله‌های متحرک (MPB) آزمایش شده است. MPB یک محک خوب برای ارزیابی کارایی الگوریتم‌­های بهینه‌­سازی در محیط­‌های پویا است. نتایج تجربی در MPB نشان می‌­دهد که روش پیشنهادی CMPCS کارایی مناسب­‌تری نسبت به روش‌­های دیگر حل مسائل بهینه‌سازی پویا دارد.
متن کامل [PDF 1031 kb]   (596 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: مقالات پردازش داده‌های رقمی
دریافت: 1398/3/7 | پذیرش: 1398/11/2 | انتشار: 1400/10/30 | انتشار الکترونیک: 1400/10/30

فهرست منابع
1. [1] S. Saremi, S. Mirjalili and A. Lewis, "Biogeography-based optimisation with chaos," Neural Computing and Applications, pp. 1077-1097, 2014. [DOI:10.1007/s00521-014-1597-x]
2. [2] S. Mirjalili, S. Mirjalili and A. Lewis, "Grey Wolf Optimizer," Advances in Engineering Software, pp. 69: 46-61, 2014. [DOI:10.1016/j.advengsoft.2013.12.007]
3. [3] D. Yazdani, B. Nasiri, A. Sepas-Moghaddam and M. Meybodi, "a novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization," Applied Soft Computing, 2013. [DOI:10.1016/j.asoc.2012.12.020]
4. [4] R. Lung and D. Dumitrescu, "A Collaborative Model for Tracking Optima in Dynamic Environments," in In: IEEE Congress on Evolutionary Computation, 2007. [DOI:10.1109/CEC.2007.4424520]
5. [5] F. Ozsoydan and A. Baykasoglu, "A multi-population firefly algorithm for dynamic optimization problems," in Evolving and Adaptive Intelligent Systems (EAIS), 2015 IEEE International Conference, 2015. [DOI:10.1109/EAIS.2015.7368777]
6. [6] X. Hu and R. Eberhart, "Adaptive particle swarm optimisation: detection and response to dynamic systems," In Congress on Evolutionary Computation, pp. 1666-1670, 2002.
7. [7] S. Sadeghi, H. Parvin and F. Rad, "Particle Swarm Optimization for Dynamic Environments," in Springer International Publishing, 14th Mexican International Conference on Artificial intelligence, MICAI, 2015.
8. [8] S. Yang and C. Li, "A clustering particle swarm optimizer for dynamic optimization," in Proc. Congr. Evol. Com, pp. 439-446, 2009.
9. [9] A. Hashemi and M. Meybodi, "Cellular PSO: A PSO for Dynamic Environments," Advances in Computation and Intelligence, pp. 422-433, 2009. [DOI:10.1007/978-3-642-04843-2_45]
10. [10] T. Blackwell, J. Branke and X. Li, "Particle swarms for dynamic optimization problems," Swarm Intelligence. Springer Berlin Heidelberg, pp. 193-217, 2008. [DOI:10.1007/978-3-540-74089-6_6]
11. [11] T. Blackwell and J. Branke, "MultiswarmT Exclusion, and Anti-Convergence in Dynamic Environment," 2006. [DOI:10.1109/TEVC.2005.857074]
12. [12] M. Kamosi, A. Hashemi and M. Meybodi, "A New Particle Swarm Optimization Algorithm for Dynamic Environments," SEMCCO, pp. 129-138, 2010. [DOI:10.1007/978-3-642-17563-3_16]
13. [13] W. Su, H. Chen, F. Liu, S. Jing and W. Li, "A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problem," Saudi Journal of Biological Sciences, pp. 695-702, 2017. [DOI:10.1016/j.sjbs.2017.01.044] [PMID] [PMCID]
14. [14] S. Nseef, S. Abdullah, A. Turky and G. Kendall, "An adaptive multi-population artificial bee colony algorithm for dynamic optimization problems," Knowledge-based Systems Center for Artificial Intelligence and Technology (CAIT), pp. 14-23, 2015. [DOI:10.1016/j.knosys.2016.04.005]
15. [15] D. Yazdani, B. Nasiri and AND..., ""mNAFSA: (2014) A novel approach for optimization in dynamic Environments with global Changes," Swarm and Evolutionary Computation, 2014. [DOI:10.1016/j.swevo.2014.05.002]
16. [16] Y. Bravo, G. Luque and E. Alba, "Global memory schemes for dynamic optimization," Springer Science,Business Media Dordrecht, 2015. [DOI:10.1007/s11047-015-9497-2]
17. [17] S. Biswas, S. Kundu, S. Das and A. Vasilakos, "Information sharing in bee colony for etecting multiple niches in non-stationary environments," 2013. [DOI:10.1145/2464576.2464588]
18. [18] S. Yang, "Associative memory scheme for genetic algorithms in dynamic environments," In Applications of Evolutionary Computing: EvoWorkshops, pp. 788-799, 2006. [DOI:10.1007/11732242_76]
19. [19] S. Yang and C. Li, "A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments," in IEEE Transactions on Evolutionary Computation, 2010. [DOI:10.1109/TEVC.2010.2046667]
20. [20] D. Yazdani, B. Nasiri, A. Sepas-Moghaddam and M. Meybodi, "novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization," Applied Soft Computing, 2013. [DOI:10.1016/j.asoc.2012.12.020]
21. [21] J. Kordestani, A. Rezvanian and M. Meybodi, ""CDEPSO: a bi-population hybrid approach for dynamic optimization problems," Applied intelligence, pp. vol. 40, pp. 682-694, 2014. [DOI:10.1007/s10489-013-0483-z]
22. [22] M. Kamosi, A. Hashemi and M. Meybodi, "A Hibernating Multi-Swarm Optimization Algorithm for Dynamic Environments," in Proceedings of World Congress on Nature and Biologically Inspired Computing, NaBIC, Kitakyush, pp. 370-376, 2010. [DOI:10.1109/NABIC.2010.5716372]
23. [23] X. Chen, D. Zhang and X. Zeng, "A Stable Matching-Based Selection and Memory Enhanced MOEDA/D for Evolutionary Dynamic Multiobjective Optimization," in Tools with Artificial intelligence (ICTAI),IEEE 27th International Conference, 2015. [DOI:10.1109/ICTAI.2015.77]
24. [24] N. Baktash and M. Meybodi, "A New Hybrid Model of PSO and ABC Algorithms for Optimization in Dynamic Environment," Int'l Journal of Computing Theory Engineering, pp. vol. 4, pp. 362-364, 2012. [DOI:10.7763/IJCTE.2012.V4.484]
25. [25] M. mojarad, H. parvin, S. nejatiyan and K. A. Bagheri , "Combining a Ensemble Clustering Method and a New Similarity Criterion for Modeling the Hereditary Behavior of Diseases," JSDP, vol. 18, no. 2, pp. 97-114, 2021.
26. [26] F. najafi, H. parvin, K. mirzaei and S. nejatiyan, "A new ensemble clustering method based on fuzzy cmeans clustering while maintaining diversity in ensemble," JSDP, vol. 17, no. 4, pp. 103-122, 2021. [DOI:10.29252/jsdp.17.4.103]
27. [27] A. Prajapati and J. Chhabra, "Harmony search based remodularization for object-oriented software systems," Computer Languages, Systems & Structures, 2017.
28. [28] X. Peng, K. Liu and Y. Jin, "A dynamic optimization approach to the design of cooperative co-evolutionary algorithms. Knowl," Based Syst, 2016. [DOI:10.1016/j.knosys.2016.07.001]
29. [29] D. Wang, F. Liu and Y. Jin, "A multi-objective evolutionary algorithm guided by directed search for dynamic scheduling," Computers & OR, 2017. [DOI:10.1016/j.cor.2016.04.024]
30. [30] W. Luo, J. Sun, C. Bu and H. Liang, "Species-based Particle Swarm Optimizer enhanced by memory for dynamic optimization," Appl. Soft Comput, 2016. [DOI:10.1016/j.asoc.2016.05.032]
31. [31] B. Yildiz, "A comparative investigation of eight recent population-based optimisation algorithms for mechanical and structural design problems," International Journal of Vehicle Design, pp. 73,1-3,208-218, 2017. [DOI:10.1504/IJVD.2017.10003412]
32. [32] B. Yildiz and H. Lekesiz, "Fatigue-based structural optimisation of vehicle components," International Journal of Vehicle Design, pp. 73, 1-3, 54-62, 2017. [DOI:10.1504/IJVD.2017.082579]
33. [33] D. Simon, EVOLUTIONARY OPTIMIZATION ALGORITHMS, 2013.
34. [34] J. Branke, "Evolutionary Optimization in Dynamic Environments," Kluwer, 2002. [DOI:10.1007/978-1-4615-0911-0]
35. [35] A. Simoes, IMPROVING MEMORY BASED EVOLUTIONARY ALGORITHM FOR DYNAMIC ENVIRONMENTS, Ph.D. Thesis, Comberia University, March., 2010.
36. [36] R. SARKER, M. MOHAMMADIAN and X. YAO, EVOLUTIONARY OPTIMIZATION, 2003. [DOI:10.1007/b101816]
37. [37] T. Blackwell, "Particle swarms and population diversity II: Experiments," GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, p. 14-18, 2003.
38. [38] Y. Jin and J. Branke, "Evolutionary optimization in uncertain environments-a survey," in IEEE Transactions on Evolutionary Computation, 2005. [DOI:10.1109/TEVC.2005.846356]
39. [39] H. Cobb, "An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments," Technical Report AIC-90-001, Naval Research Laboratory, Washington, 1990. [DOI:10.21236/ADA229159]
40. [40] F. Vavak, T. Fogarty and K. Jukes, "A genetic algorithm with variable range of local search for tracking changing environments," In Parallel Problem Solving from Nature, pp. 376-385, 1996. [DOI:10.1007/3-540-61723-X_1002]
41. [41] f. Vavak and k. Jukes, "Performance of a genetic algorithm with variable local search range relative to frequency for the environmental changes," in In International Conference on Genetic Programming, 1998.
42. [42] J. Grefenstette, "Genetic algorithms for changing environments," In Parallel Problem Solving from Nature, pp. 137-144, 1992.
43. [43] J. Grefenstette and L. Connie, "An approach to anytime learning," in In International Conference on Machine Learning, 1992. [DOI:10.1016/B978-1-55860-247-2.50029-2]
44. [44] N. Mori, H. Kita and Y. Nishikawa, "Adaptation to a changing environment by means of the thermodynamical genetic algorithm," In Parallel Problem Solving from Nature, pp. 513-522, 1996. [DOI:10.1007/3-540-61723-X_1015]
45. [45] W. Cedeno and R. Vemuri, "On the use of niching for dynamic landscapes," 1997.
46. [46] S. Yang, Genetic algorithms with elitism-based immigrants for changing optimization problems, In Applications of Evolutionary Computing: EvoWorkshops, 2007, pp. 627-636. [DOI:10.1007/978-3-540-71805-5_69]
47. [47] C. Ryan, "Diploidy without dominance," In Nordic Workshop on Genetic Algorithms, pp. 45-52, 1997.
48. [48] S. Yang, "Explicit memory schemes for evolutionary algorithms in dynamic environments," In Evolutionary Computation in Dynamic and Uncertain Environments. Springer, 2007. [DOI:10.1007/978-3-540-49774-5_1]
49. [49] S. Yang., "Non-stationary problems optimization using the primal-dual genetic algorithm," In Congress on Evolutionary Computation, pp. 2246-2253, 2003.
50. [50] A. Younes, "Adapting Evolutionary Approaches For Optimization in Dynamic Environments," A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Doctor of Philosophy in Systems Design Engineering, Waterloo, Ontario, Canada, 2006.
51. [51] R. Morrison, Performance Measurement in Dynamic Environments, 2015.
52. [52] M. Zarei, H. Parvin and M. Dadvar, "A New Method to Optimize Dynamic Environments with Global Changes Using the Chickens-Hen' Algorithm," Springer International Publishing AG, pp. 331-340, 2017. [DOI:10.1007/978-3-319-62428-0_26]
53. [53] A. Simoes, "Improving Memory Based Evolutionary Algorithm for Dynamic Environments, Ph.D. Thesis, Comberia University, March., 2010.

ارسال نظر درباره این مقاله : نام کاربری یا پست الکترونیک شما:
CAPTCHA

ارسال پیام به نویسنده مسئول


بازنشر اطلاعات
Creative Commons License این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است.

کلیه حقوق این تارنما متعلق به فصل‌نامة علمی - پژوهشی پردازش علائم و داده‌ها است.